Advertisement

Distributed and Parallel Databases

, Volume 37, Issue 4, pp 587–622 | Cite as

Algorithms and framework for computing 2-body statistics on GPUs

  • Napath PitaksiriananEmail author
  • Zhila Nouri Lewis
  • Yi-Cheng Tu
Article
  • 88 Downloads

Abstract

Various types of two-body statistics (2-BS) are regarded as essential components of low-level data analysis in scientific database systems. In relational algebraic terms, a 2-BS is essentially a Cartesian product between two datasets (or two instances of the same dataset) followed by a user-defined aggregate. The quadratic complexity of these computations hinders timely processing of data. Use of modern parallel hardware has thus become an obvious solution to meet such challenges. This paper presents our recent work on designing and optimizing parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). Although a typical 2-BS problem can be summarized into a straightforward parallel computing pattern, traditional knowledge from (general) parallel computing often falls short in delivering the best possible performance. Therefore, we present a suite of techniques to decompose 2-BS problems and methods for effective use of computing resources on GPUs. We also develop analytical models that guide us towards finding the best parameters of our GPU programs. As a result, we achieve the design of highly-optimized 2-BS algorithms that significantly outperform the best known GPU and CPU implementations. Although 2-BS problems share the same core computations, each 2-BS problem however carries its own characteristics that calls for different strategies in code optimization. For that, we develop a software framework that automatically generates high-performance GPU code based on a few parameters and short primer code input. We further present two case studies to demonstrate that code generated by this framework reaches a very high level of efficiency.

Keywords

2-Body statistics Parallel computing GPGPU GPU CUDA 

Notes

Acknowledgements

This work is supported by an award (IIS-1253980) from the National Science Foundation (NSF) of U.S.A.. Equipments used in the experiments are partially supported by another grant (CNS-1513126) from the same agency.

References

  1. 1.
    Türker, C., Akal, F., Studer-Joho, D., Schlapbach, R.: B-fabric: An open source life sciences data management system. In: Scientific and Statistical Database Management, 21st International Conference, SSDBM 2009, New Orleans, LA, USA, 2–4 June 2009, Proceedings, pp. 185–190 (2009)Google Scholar
  2. 2.
    Feig, M., Abdullah, M., Johnsson, S.L., Pettitt, B.M.: Large scale distributed data repository: design of a molecular dynamics trajectory database. Future Gener. Comp. Syst. 16(1), 101–110 (1999)CrossRefGoogle Scholar
  3. 3.
    Finocchiaro, G., Wang, T., Hoffmann, R., Gonzalez, A., Wade, R.C.: DSMM: a database of simulated molecular motions. Nucleic Acids Res. 31(1), 456–457 (2003)CrossRefGoogle Scholar
  4. 4.
    Xu, W., Ozer, S., Gutell, R.R.: Covariant evolutionary event analysis for base interaction prediction using a relational database management system for RNA. In: Scientific and Statistical Database Management, 21st International Conference, SSDBM 2009, New Orleans, LA, USA, 2–4 June 2009, Proceedings, pp. 200–216 (2009)Google Scholar
  5. 5.
    Luo, S., Gao, Z.J., Gubanov, M.N., Perez, L.L., Jermaine, C.M.: Scalable linear algebra on a relational database system. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, 19–22 April 2017, pp. 523–534 (2017)Google Scholar
  6. 6.
    Tu, Y.-C., Chen, S., Pandit, S.: Computing distance histograms efficiently in scientific databases. ICDE, pp. 796–807 (2009)Google Scholar
  7. 7.
    Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.): Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Rokach, L., Kisilevich, S.: Initial profile generation in recommender systems using pairwise comparison. IEEE Trans. Syst. Man Cybern C 42(6), 1854–1859 (2012)CrossRefGoogle Scholar
  9. 9.
    Jiang, S., Wang, X., Zhu, H.: Learning pairwise comparisons of items with bigram content features for recommending. In: 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT), pp. 446–449 (2013)Google Scholar
  10. 10.
    He, B., Yang, K., Fang, R., Lu, M., Govindaraju, N., Luo, Q., Sander, P.: Relational joins on graphics processors. In: Procs. ACM Intl. Conf. Management of Data (SIGMOD), pp. 511–524 (2008)Google Scholar
  11. 11.
    NVIDIA: CUDA C Programming Guide Version 7.0.Google Scholar
  12. 12.
  13. 13.
    Gray, A.G., Moore, A.W.: N-body problems in statistical learning. In: Advances in Neural Information Processing Systems (NIPS), pp. 521–527 (1993)Google Scholar
  14. 14.
    Zhu, Y., Zimmerman, Z., Shakibay Senobari, N., Yeh, C.-C.M., Funning, G., Mueen, A., Brisk, P., Keogh, E.: Exploiting a novel algorithm and gpus to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins. Knowl. Inf. Syst. 54, 203 (2017)CrossRefGoogle Scholar
  15. 15.
    Stratton, J.A., Rodrigues, C., Sung, I.-J., Chang, L.-W., Anssari, N., Liu, G., Hwu, W.-M., Obeid, N.: Algorithm and data optimization techniques for scaling to massively threaded systems. Computer 45(8), 26–32 (2012)CrossRefGoogle Scholar
  16. 16.
    Levine, B.G., Stone, J.E., Kohlmeyer, A.: Fast analysis of molecular dynamics trajectories with graphics processing units-radial distribution function histogramming. J. Comput. Phys. 230, 3556–3569 (2011)CrossRefGoogle Scholar
  17. 17.
    Jensen, B., Saez Gallego, J., Larsen, J.: A predictive model of music preference using pairwise comparisons. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1977–1980 (2012)Google Scholar
  18. 18.
    NVIDIA GeForce Tesla V100 WhitepaperGoogle Scholar
  19. 19.
    Nvidia’s next generation cudatm compute architecture:fermi: NVidia Developer Technology, Tech. RepGoogle Scholar
  20. 20.
    Nvidia’s next generation cudatm compute architecture:kepler gk110: NVidia Developer Technology, Tech. RepGoogle Scholar
  21. 21.
    NVIDIA. GTX 980 whitepaperGoogle Scholar
  22. 22.
    NVIDIA GeForce GTX 1080 WhitepaperGoogle Scholar
  23. 23.
    Agrawal, A., Huang, X.: Pairwise statistical significance of local sequence alignment using sequence-specific and position-specific substitution matrices. IEEE/ACM Trans. Comput. Biol. Bioinform. 8, 194–205 (2011)CrossRefGoogle Scholar
  24. 24.
    NVIDIA. CUDA C Best Practices Guide, version 7.5Google Scholar
  25. 25.
  26. 26.
    Wong, H., Papadopoulou, M., Sadooghi-Alvandi, M., Moshovos, A.: Demystifying GPU microarchitecture through microbenchmarking. In: IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2010, 28–30 March 2010, pp. 235–246. White Plains, NY, USA (2010)Google Scholar
  27. 27.
    Wang, J., Xie, X., Cong, J.: Communication optimization on GPU: a case study of sequence alignment algorithms. In: 2017 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2017, Orlando, FL, USA, 29 May–2 June 2017, pp. 72–81 (2017)Google Scholar
  28. 28.
    Li, H., Yu, D., Kumar, A., Tu, Y.: Modeling in cuda strems—a means for high-throughput data processing. In: Big Data (Big Data, IEEE International Conference, pp. 301–310 (2014)Google Scholar
  29. 29.
    Bloom, D.: A birthday problem. Am. Math. Mon. 80, 1141–1142 (1973)CrossRefGoogle Scholar
  30. 30.
    Rui, R., Tu, Y.: Fast equi-join algorithms on gpus: Design and implementation. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Chicago, IL, USA, 27–29 June 2017, pp. 17:1–17:12 (2017)Google Scholar
  31. 31.
  32. 32.
    Rui, R., Li, H., Tu, Y.: Join algorithms on GPUs: A revisit after seven years. In: 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara, CA, USA, October 29–November 1, 2015, pp. 2541–2550 (2015)Google Scholar
  33. 33.
    Govindaraju, N.K., Lloyd, B., Wang, W., Lin, M., Manocha, D.: Fast computation of database operations using graphics processors. In: Procs. ACM Intl. Conf. Management of Data (SIGMOD), ser. SIGMOD ’04, pp. 215–226 (2004)Google Scholar
  34. 34.
    He, B., Luo, Q.: Cache-oblivious nested-loop joins. In: Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management, Arlington, Virginia, USA, 6-11 Nov 2006, pp. 718–727 (2006)Google Scholar
  35. 35.
    Kim, C., Sedlar, E., Chhugani, J., Kaldewey, T., Nguyen, A.D., Blas, A.D., Lee, V.W., Satish, N., Dubey, P.: Sort vs. hash revisited: fast join implementation on modern multi-core cpus. PVLDB 2(2), 1378–1389 (2009)Google Scholar
  36. 36.
    Albutiu, M., Kemper, A., Neumann, T.: Massively parallel sort-merge joins in main memory multi-core database systems. PVLDB 5(10), 1064–1075 (2012)Google Scholar
  37. 37.
    Ponce, R., Cardenas-Montes, M., Rodriguez-Vazquez, J.J., Sanchez, E., Sevilla, I.: Application of gpus for the calculation of two point correlation functions in cosmology. In: ADASS XXI (Paris, 2011) Conference Proceedings (2012)Google Scholar
  38. 38.
    Karnagel, T., Müller, R., Lohman, G.M.: Optimizing gpu-accelerated group-by and aggregation. In: International Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures—ADMS 2015, Kohala Coast, Hawaii, USA, 31 Aug 2015, pp. 13–24 (2015)Google Scholar
  39. 39.
    Ye, Y., Ross, K.A., Vesdapunt, N.: Scalable aggregation on multicore processors. In: Proceedings of the Seventh International Workshop on Data Management on New Hardware, DaMoN 2011, Athens, Greece, 13 June 2011, pp. 1–9 (2011)Google Scholar
  40. 40.
    Kumar, A., Grupcev, V., Yuan, Y., Huang, J., Tu, Y., Shen, G.: Computing spatial distance histograms for large scientific data sets on-the-fly. IEEE Trans. Knowl. Data Eng. 26(10), 2410–2424 (2014)CrossRefGoogle Scholar
  41. 41.
    Grupcev, V., Yuan, Y., Tu, Y., Huang, J., Chen, S., Pandit, S., Weng, M.: Approximate algorithms for computing spatial distance histograms with accuracy guarantees. IEEE Trans. Knowl. Data Eng. 25(9), 1982–1996 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA

Personalised recommendations